(SpringerBriefs in Business Process Management) Learning Analytics Cookbook_ How to Support Learning Processes Through Data Analytics and Visualizatio

3.4 Ethical Frameworks in Learning Analytics 25
that can be used for learning analytics together and aggregating the information from
those sources. Such an effort bears particular ethics and privacy risks, so during the
project a comprehensive ethics and privacy framework was developed (Steiner et al.
2016). A particular emphasis of this framework lies on automatic analytics algorithms
that aggregate and process the data and result in certain higher-level assessments
and performance predictions that are far beyond descriptive statistics. Steiner
et al. (2016) observed that the main algorithms that are already applied in practice
did not undergo thorough validation studies, so the algorithms’ results and predictions
validity and reliability cannot be guaranteed. The framework includes eight
aspects:
Data Privacy In principle, not all information that may be available in digital form
should be accessible to teachers. One must consider in the conceptualization phase of
a learning analytics system which data should be accessed and made part of analytics
(e.g., discarded working steps outside the time when work assignments were
submitted).
Purpose and Ownership In the conceptualization phase, the concrete purposes and
goals of a learning analytics system must be made clear. A data-mining approach—
that is, collecting all possible data and determining later on what can be done with
it—is not acceptable. Who owns the data must also be precisely set out. For example,
organizations may own final aggregations and analytics (e.g., grades), while the
individual learners still own all the raw data that led to these results.
Consent While consent is an important good from an ethics point of view, consent
is not always possible in school contexts, where the ability to opt out is not practical.
Broad consent of the majority of students or their legal representatives can facilitate
the establishment of a common set of organizational practices.
Transparency and Trust Stakeholders’ trust in the system’s functions and outcomes
is the most valuable asset for a learning analytics system that evaluates
performance. A learning analytics solution should make its principles and analytics
algorithms appropriately transparent to all stakeholders, and learners should have the
option to disagree with the results of analytics and the opportunity to negotiate
results. (Such is the case in persuadable open learner modelling systems.)
Access and Control Related to the aspect of transparency, access and control can
strengthen the trust in a learning analytics system. Specifically, learners should have
the ability to access the information gathered about them easily and to correct the
data if necessary.
Accountability and Assessment Organizations that set up learning analytics solutions
should be accountable, so who is responsible for what should be clear and
public. The functions, the validity, and the impact of the results should be evaluated
frequently so erroneous analytics and misuse of outcomes can be identified.
Data Quality Learning analytics is based solely on data from various resources,
and these data are usually incorrect and incomplete to a degree. In the context of the